• Course title: Project management
• Number of ECTS: 4
• Course code: MSPC-1
• Module(s): 1.1 COMMON CORE
• Language: EN
• Mandatory: Yes

Getting familiar with the basics of project management.

Being able to organize projects and the related activities to set up a PM team.

History of PM, Basic definitions, PM Processes, Time scheduling, Human factor in PM, establishing team work, leadership and conflict solving.

Written exam (100%)

Participation in both block lectures and integrated exercises is mandatory.

Literature & resources

Scriptum of Lecture and related exercises (will be submitted during the lecture).

• Course title: Programming for engineers (Matlab & Python)
• Number of ECTS: 4
• Course code: MSPC-2
• Module(s): 1.1 COMMON CORE
• Language: EN
• Mandatory: Yes

Understanding the MATLAB/Python environment.

Being able to do execute codes/files using MATLAB/Python.

Being able to carry out simple numerical computations and analyses using MATLAB and Python.

After attending this class, the students will be able to write algorithms with functions and scripts to solve engineering problems. The students can solve mathematical problems and manipulate matrices and vectors. They will learn the differences between scripts and functions. They will learn the repetitive and condition statements. The students can build a Graphic User Interface (GUI) They will acquire skill on data plotting, animation and 3D graphics. They will learn how to solve linear and non linear systems.

Introduction to concepts of programmingDatatypes and VariablesOperators and ExpressionsLoopsString ManipulationPlotting/Data Visualization in 2D and 3DGUI

4 tasks counting each for 25% of the final grade

Task 1:

Submit a project incorporating the concepts taught during the lecture (Using Matlab)

The assessment will be done based on

The complexity of the topic selected to submit as project

The quality of code (efficiency, readability and executability)

Task 2:

Submit the assignment given by solving the questions (Using python)

The assessment will be done based on

Solution submitted by the student (the technique used)

The readability, executability and efficiency of the code)

Task 3:

Continuous assessment test taken during the lab hours (Matlab)

Multiple choice questions will be given to be solved by the students in the stipulated time

Each question will carry some marks depending on the difficulty level.

Task 4:

Continuous assessment test taken during the lab hours (Matlab)

Programming questions will be given to the students to be solved in the stipulated time.

Each question will carry some marks depending on the difficulty.

Grades will be awarded based on the solution obtained, the quality of code and the efficiency of the program.

Literature & resources

The course notes and slides provided during the lectures. Additional resources will be given during the lecture.

• Course title: Supply chain & logistics
• Number of ECTS: 4
• Course code: MSPC-3
• Module(s): 1.1 COMMON CORE
• Language: EN
• Mandatory: Yes

Provide knowledge and insight into supply chain systems as a whole (manufacturing, distribution, retail and customer demand), understand the critical infrastructure for the production and distribution, understand decision making issues in logistics and supply chain management in general and in terms of sustainability, understand the opportunities and constraints of digitalization in supply chain management (internet of things, digital documents, monitoring and alerting), apply conceptual, analytical and numerical tools for modeling and solving logistics and supply chain applications, give insight into network economics and system dynamics in supply chains. 

At the end of the course, students are able to explain the role and the meaning of logistics and supply chain systems in modern sustainable production and logistics networks, to describe the relations between the different players and how these systems work from supplier to customers, to manage simple networks in terms of efficient and green value creation in order to maximize the overall profit in the system, to apply both basic operational and strategic methods of supply chain management.

Introduction (basic concepts and definition), Strategic fit (make-or-buy, cost, responsiveness, agility), Supply Chain integration (material flow, push/pull and lead time concept), Facility location problems, Vehicle routing problems (e.g. travel salesman problem), Supply chain network design and equilibrium, Systems dynamics and value of coordination, Transport and distribution, Forecasting, Sales and Operations Planning, Capacity and Inventory Management, Material Requirements Planning , SCM policies and performance indicators, Sustainability and Reverse logistics, Digitalization in SCM.

Written exam (100%)

Assessment based on written answers to a test using instructional tasks or questions (short question tests, case studies, essays), composed during 90 minutes of time on-site at the end of the term. Any language dictionary as well as a non-programmable calculator will be allowed during the exam. 

Literature & resources

•Chopra, S. (2019): Supply Chain Management: Strategy, Planning, and Operation, 7th ed., Upper Saddle NJ, Pearson, Boston, 2019•Christopher, M. (2022): Logistics & Supply Chain Management, 6th edition, FT Publishing International, Upper Saddle NJ, 2022•MacCarthy, B., Ivanov, D. (2022): The Digital Supply Chain, 1st edition, Elsevier, Amsterdam, 2022•Jacobs, F. R., Chase, R. B. (2018): Operations and Supply Chain Management, 15th Global Edition, McGraw-Hill Education, New York, 2018•Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E. (2008): Designing and managing the supply chain, 3rd edition, McGraw Hill, New York, 2008•Articles from literature•Hand-outs

• Course title: Life Cycle Assessment and Eco Design
• Number of ECTS: 3
• Course code: MSPC-4
• Module(s): 1.1 COMMON CORE
• Language: EN
• Mandatory: Yes

Students of this course learn to design products/megastructures following the principles of sustainability. For that, students get to know what sustainable products and sustainable resources can mean. Additionally, students understand how a product’s performance for sustainability can be assessed in order to critically reflect on it. Particularly, the course aims at enabling students to apply life cycle assessment (LCA) and eco-design methods.

After successfully participating in the course, students will be able to1.) independently improve the environmental performance of their products/megastructures and developing sustainable product concepts by applying eco-design strategies, principles and methods in the early stages of the development process,2.)  integrate the ecological perspective in the technical product creation, and3.) conduct their own LCA studies.

The content of the course focuses on the following main areas: – Introduction to sustainable development and related concept; – The importance of life cycle thinking; – The life cycle of products, services and megastructures; – Environmental impacts and their indicators; – Eco-design strategies, principles and methods; – Manual calculation of LCA; – Practical issues of LCA; – Critical review of LCA studies; – LCA and eco-design in early stages of the development process.

Quiz 1 / Written exam (33.33%)Quiz 2 / Written exam (33.33%)Quiz 3 / Written exam (33.33%)Objective of the assignments: apply theoretical knowledge (concepts, categories) from previous lectures.Assessment criteria: quality of argumentation why concepts and categories have been applied.Assessment rules: independent and original work, submission on time.

Literature & resourcesBaumann, H; Tillman, A-M: The Hitch Hiker’s Guide to LCA: An Orientation in Life Cycle Assessment Methodology and Applications. Professional Pub. Service 2004  Crul, M.R.M; Diehl J.C: Design for Sustainability: A Step-by-Step Approach. United Nations Environment Programme 2009

• Course title: Assembly and testing technologies
• Number of ECTS: 4
• Course code: MSPC-5
• Module(s): 1.1 COMMON CORE
• Language: EN
• Mandatory: Yes

The automotive industry is leading in advanced assembly and testing technologies.

The student will understand assembly, testing and adjustment processes at a vehicle final assembly plant and be able to apply this knowledge to future engineering tasks.

The student will understand assembly technologies and related testing and adjustments in regard to the requirements of the vehicle.

They can evaluate product design for assembly.

The students know machinery and equipment for manual, semi-automated and automated assembly, testing and adjustment processes and are aware about the specific opportunities and limitations. 

1. Key performance indicators for production machines in the final assembly plant• Tolerances and capability• Availability• Cycle time and volume• Quality management2. OOA method to work out modular concepts for production machines in the final assembly plant in regard to an efficient production of the vehicle  3. Structure of modern final assembly plants• Principles of planning assembly plants• Structures of assembly systems, e.g. assembly lines• Structures of End of Line test and adjustment systems4. Principles of assembly stations in regard to vehicle requirements• Examples “rear axle assembly” and “marriage” stations• Fixation technologies • Virtual Commissioning• Manual, semi-automatic and automatic processes  • Conveyors, manual and automatic handling devices and robotic5. Principles of End of Line testing and adjustment stations in regard to vehicle requirements• Basics of linear algebra and vector calculation• Wheel alignment• Head lamp and driver assistance systems setting  • Roll-, brake-, ABS-testing• Manual, semi-automatic and automatic processes and adjustment devices• Optical sensor technologies

Written exam (100%)

Knowledge in regard to the lessons.

Literature & resources

Literature, Patents and Norms

Mechern, 14.06.2024, Dr. T. Tentrup

• Course title: Assessment of Finite Element Calculations
• Number of ECTS: 3
• Course code: MSPC-6
• Module(s): 1.2 MECHANICS
• Language: EN
• Mandatory: No

On completion of the course unit successful students will be able to use and understand a well-established norm [1] for analytical strength assessment of components based on local stresses calculated with the help of the finite element method.

As this norm is well known in industry and research (6th edition), its use and the respective background are detailed in this lecture. The student understands why the norm imposes a specific procedure for static and a different one for fatigue assessment and what the relevant influence factors are. All important background information is given by two classical textbooks [2], [4], multiple handouts and three discussed examples. The additional information deploys the relevant physical background phenomena quantitatively, where the norm is short. Vice-versa, the textbooks etc. do not contain numerical quantity values for direct use, what the only norm does.

When a finite element calculation has been performed and the stresses have been calculated they have to be assessed with respect to the imposed stress limits depending on the charging of the component:

Static assessment based on linear and non-linear Finite Element Method (FEM)

Fatigue assessment based on linear stress and strain calculations

Lecture 1: Introduction to the problem (General survey of FKM, Chapt. 0, [1]) and by discussion of an analytical example: stress distribution in a thick walled pressurized tube. Repetition of principle stresses and three stress hypotheses for combined stress. Difference between Fatigue Assessment and Fracture Mechanics (1.handout 7 pages, fracture mechanics not part of this lecture), definition of local and nominal stresses (assessment by use of nominal stresses is not part of this lecture).Lecture 2: Definition of local, uniaxial, multiaxial, proportional, synchronous and non-proportional stresses. Procedure of calculation and demarcation with respect to nominal stresses, repetition of basics: effect of notches, stress concentration factor SCF or KtLecture 3: Chapt. 3.0 – 3.1.2.2 of FKM [1]: combined stress in case of brittle and ductile material, multiaxiality, repetition of basics: stress-strain curve, simplification of elastic-ideal plastic behavior, hardening, real stress and real strain, yield-curve of a component, (2.handout 1 page, reinforced concrete), section factor, stress and strain distribution of a smooth specimen subject to bending, NEUBER equation with example (3.handout 1 page), plastic strain limits vs. elongation at break, plastic limit loading.Lecture 4: Chapt. 3.1.2.2 – 3.2.1.2 of FKM [1] : effect of thickness and repetition of basics: plain stress sate and plain strain state, full plasticity and collapse loading, effect of pre- or residual-stress for brittle and ductile material, loading and unloading, reverse-plastification, (4.handout 1 page, effect of post-weld-heat-treatment)Lecture 5: Chapt. 3.2.1.2 – 3.6.1.2 of FKM [1]: effect of thickness, elevated temperature including creep (5. handout 2 pages), section factor npl of FKM based on NEUBER rule, plastic notch factor and strain limit, typical safety factors and assessment incl. multiaxiality; definition of stress categories: primary & secondary, membrane, bending & peak stresses only to demark from ASME-code approach (not part of the lecture), repetition of basics: failure load of brittle and ductile material, Charpy-impact testing. Lecture 6: Chapt. 4.0 – 4.1.3.1 of FKM [1] : s-n-line, stress ratio R, stress spectrum, endurance limit, slope k, and repetition of basics: cyclic loading, proportional, synchronous and non-proportional loading, finite life and endurance limit, stress-range R and the s-n-line (Wöhlerline), ‘slope’ of the s-n-line, knee point, typical scatter Tn and Ts values, statistics of cyclic testing and normalized s-n-lineLecture 7: Chapt. 4.1.3.1 – 4.1.3.2 of FKM [1] : constant amplitude s-n-curve, mean stress influence, and repetition of basics: alternating and pulsating loading, endurance strength limits for different materials and loadings, effect of mean stress- Haigh and Smith diagram, mean stress sensitivity, simplified Haigh diagram acc. to FKM, static limits of the Haigh-diagram, effect of surface, size, stress gradient (or volume) and corrosive environment on the endurance limit, effect of notches, definition of fatigue notch factor Kf vs. form factor Kt (=SCF), dynamic support factor= Kt – Kf-ratioLecture 8: Chapt. 4.1.3 – 4.6.2.2 of FKM [1]: influence of mean stress and variable amplitude, fatigue limit=endurance limit, temperature influence, 6. handout 1 pages to repeat support factor= Kt – Kf-ratio, related stress gradient, design factor KWK, mean stress factor KAK, variable amplitude fatigue strength factor KBK, the different  fields of the HAIGH diagram, two simplified models of s-n-lines, Miner´s elementary and consistent rule, damage sum, degree of utilisation, stress spectrum and its determination by rainflow- and rainfill=reservoir-counting (example with 7. handout -5 pages )Lecture 9: First full example, based on our open access-peer-reviewed publication [3] (8 . handout – 7 pages )Lecture 10: Chapt. 6.0 – 6.2.2 of FKM [1]: Discussion of two fully detailed examples in the annexe of FKMLecture 11: ANSYS-Workbench, computer room: introduction into the software, modelling of a thick walled tube (Stephan Sellen)Lecture 12: ANSYS-Workbench, computer room: 9 . handout, send by email – geometry of first example, ref. to lecture 9, (Stephan Sellen) )Lecture 13: ANSYS-Workbench, computer room: full linear and non-linear calculation acc. to FKM for the example of lecture 9, repetition and summary of theory (10 . handout – 7 pages, (Stephan Sellen) )Lecture 14: repetition and summary, Q+A, exam preparation

Written exam (100%)

 

Literature & resources

[1] FKM Guideline, 6th Edition 2012, Analytical Strength Assessment of Components, ISBN 978-3-8163-0649-8[2] Fundamentals of Machine Elements, SI Version, 3rd Edition, CRC-Press, ISBN 978-1-4822-4748-0[3] Design rules for autofrettage of an aluminum valve body; S. Sellen, S. Maas, T. Andreas, P. Plapper, A. Zürbes and D. Becker, http://onlinelibrary.wiley.com/doi/10.1111/ffe.12328/abstract[4] Issler, Ruoss, Häfele, Festigkeitslehre – Grundlagen, Springer, ISBN 3-540-40705-7 10 handouts during the lectures (in English)

• Course title: CAD & CAE
• Number of ECTS: 4
• Course code: MSPC-7
• Module(s): 1.2 MECHANICS
• Language: EN
• Mandatory: No

The main objectives are:

1-Development of a professional knowledge in technical communication tools available in actual design offices. The focus will be on understanding the different methodology of numerical model creation, using full 3D parametric features capabilities.

2- Validation of parts and systems.

3- Comprehension and production of Engineering drawings for use in different Engineering specialisations.

The students in the defined scope of the course can:

1- professionally work with a commonly used commercial CAD and FEA software

2- validate structurally simple parts, assemblies and mechanical concept

3- Generate parametric design of Products and Systems including sustainability constraints

4- Communicate technical concepts using industrial modern tools, and to understand the various standards and practices in the mechanical industrial field

1- Introduction to CAD 2- Feature modelling / advanced sketch 3- Parametric Modelling 4- structural simulation 5- Validation of parts and systems 6- Optimisation of geometry and material 7 Overall capabilities of CAE software

Final written exam

Literature & resources

Inventor Professional & Revit Documentation and Online help

MemoTech – Dessin Technique et norm CAO – C.Hazard

Precis de construction mecanique – R.Quatremer – Nathan

Guide du Dessinateur industriel – A.Chevalier – Hachette

Technisches Zeichnen – Hoischen – Edition Cornelsen / Girardet

“Fundamentals of Machine Elements, Third Edition: SI Version”, Steven R. Schmid, Bernard J. Hamrock, Bo. O. Jacobson.

“Fundamentals of Machine Components Design”, R. C. Juvinall, Kurt M. Marshek.

„Roloff/Matek Maschinenelemente“, Herbert Wittel, Dieter Muhs, Dieter Jannasch, Joachim.Voßiek.

“Engineering Drawing and Design”,  5th Edition, David A. Madsen, David P. Madsen

“Fundamentals of Machine Elements, Third Edition“, Steven R. Schmid, Bernard J. Hamrock,  Bo. O. Jacobson · 

 “Fundamentals of Machine Components Design “ , R. C. Juvinall, Kurt M. Marshek

“ Mark’s Calculations for Machine Design “ , Thomas Brown.

“ Shigley’s Mechanical Engineering Design“, Richard G Budynas, Keith J Nisbett.

• Course title: Machine design
• Number of ECTS: 4
• Course code: MSPC-8
• Module(s): 1.2 MECHANICS
• Language: EN
• Mandatory: No

The aims of the course are:+ to deepen knowledge of designs of machine elements gained in the courses of  machine elements in bachelor study+ to present advanced design methods of mechanical parts+ to introduce advanced tools (CAE) for analyses of machine design: FEA – ANSYS/ Inventor, CAD – Inventor, reporting/ calculations – Mathcad, and Fusion 360 – CAD cloud computing+ to build base student knowledge of machine design, which is needed for their projects in semester 2 – Machine Design Exercise.

After the course,  the student:+ is able to carry out a design process for mechanical objects+ uses in practice analytical equations of mechanics to design machine elements+ solves real technical problems using previously acquired knowledge of subjects: mechanics, strength of materials, machine element design, and CAD+ is able to propose an appropriate technological process of manufacture and assembly for a particular machine element+ knows how to utilize CAE tools like ANSYS, Inventor, and Mathcad in design projects+ is able to understand the concept of machine element optimization and employ this method in projects.

Part I Fundamentals+ Tolerances and fits. Deviations of form and position and surface roughness+ Loads, analysis, materials, static body stresses+ Fatigue and impact+ Safety factor, reliabilityPart II — Machine Elements+ Stresses and deformations in cylinders+ Shafts and associated parts+Bearings+ General gear theory+ Spur sears, helical, bevel and worm gears+ Manual gearboxes designs+ Brakes and clutches+ Flexible machine elements+ Belts, wire ropes, rolling chains+ Machine element optimization

Written exam (100%)

The written exam consists of 3 problems to solve; each problem will be assessed, taking into account a used methodology – 60%, and numerical correctness of the solution – 40%. 

Literature & resources

“Fundamentals of Machine Elements, Third Edition“, Steven R. Schmid, Bernard J. Hamrock, Bo. O. Jacobson Course materials available on Moodle systemSupplementary: “Fundamentals of Machine Components Design“, R. C. Juvinall, Kurt M. Marshek “Mark’s Calculations for Machine Design“, Thomas Brown. “Shigley’s Mechanical Engineering Design“, Richard G Budynas, Keith J Nisbett.“Engineering Drawing and Design“, 5th Edition, David A. Madsen, David P. Madsen

• Course title: Sensors & signal processing
• Number of ECTS: 3
• Course code: MSPC-9
• Module(s): 1.3 ELECTRICAL AND COMPUTER ENGINEERING (ECE)
• Language: EN
• Mandatory: No

Understand the basic metrology, sensor concepts and signal processing methods in the context of engineering.

Gain a basic understanding of sensor and signal processing concepts.

Basic SI units, electrical circuits, linear time-invariant systems, Fourier analysis, analog and digital sensor data acquisition, introduction to fundamental sensor types.

Active participation (10%)Objectives: Encouraging students in class activities to improve their thinking skills and introduce new concepts to their peers.Assessment criteria: Student performance and participation in the classroomTake-home assignment (40%)Objectives: Examination of students’ skills and understanding of the material taught in class.Assessment rules: The assignments must be completed and submitted by the deadline. Assessment criteria: The evaluation depends on understanding of the subject and the concept, the final results, mathematical approach, etc.Written exam (50%)Objectives: Test the knowledge, skills, and aptitude acquired by the students.Assessment rules: In-class test of various types of questions, 2-hour duration. To pass the course the student needs to score at least 40% in the final exam and have gain at least 10 points out of 20 in total.Assessment criteria: The mathematical approach, presentation, and the final answer. Mandatory attendance.

Literature & resourcesLecture slidesOwn written notes from lecturesOptional:Jacob Fraden, Handbook of Modern Sensors: Physics, Designs, and Applications, Springer 2010, ISBN: 978-1441964656 

• Course title: Technical Energy Systems Modeling and Simulation
• Number of ECTS: 4
• Course code: MSPC-10
• Module(s): 1.3 ELECTRICAL AND COMPUTER ENGINEERING (ECE)
• Language: EN
• Mandatory: No

Knowledge of how to model and simulate dynamic system.* Build mathematical models for dynamics of technical systems derived from basic principles * Use advanced tools for numeric and symbolic computing * Apply decomposition, transformation and approximation methods * Elaborate a case study and present computational results.

Learning how to apply simulation methods on given practical problems, having different types of technical dynamic (energy) systems. In the seminar, techniques for modeling of technical systems are elaborated in case studies for typical technical systems employing symbolic and numeric computation methods.

Theoretical basis lecture, hands on workshops with Matlab.1 Technical Systems2 System Structures and Model Descriptions3 Continuous Models from Variational Analysis4 Model Simplification5 Optimal System Operation.

Take-home assignment (30%)

Seminar simulation work.

Assessments will be given on weekly bases in first 6 week, after that self-study and autonomous working on single or group projects.

Oral exam (70%)

Literature & resources

Kondipudi, Prigogine : Modern Thermodynamics, Wiley&Son, 1998

Baumann : Symmetry Analysis of Differential Equations, Springer Verlag, 2000

Ljung, Glad : Modelling of Technical Systems, Prentice Hall, 1995

Wellsted: Introduction to Physical Modeling, Control Systems Principles, 2000

• Course title: Networking
• Number of ECTS: 3
• Course code: MICS2-8
• Module(s): 1.3 ELECTRICAL AND COMPUTER ENGINEERING (ECE)
• Language: EN
• Mandatory: No

Split into two parts, part (A) will introduce fundamental aspects of generic mathematical networks (graphs), usually summarized as network science, and not limited to computer networking, but also applicable to networks in social media, machine learning (artificial neural networks), road traffic and vehicle communication, cryptocurrency transactions, energy transport, biology, citation networks and many more.Part (B) will explain model fundamentals of quantum networking, from basic qubit notations, Bell states, entanglements, 2-qubit gates, until quantum repeaters using entanglement swapping for multi-hop quantum communication networks.

At the end of the course, students will be able to identify and apply suitable models and graph representations with related graph metrics to analyse practical networked scenarios, and reason about most efficient application of (network centrality) metrics. Students will understand power and limits of end-to-end concepts of quantum networking applied to distributed communication scenarios including entanglement swapping.

Part (A): Eigenvectors and eigenvalues, adjacency matrix, hypergraphs and bipartite networks, trees and planar networks, degrees, paths and components, graph Laplacian.Part (B): Introduction to quantum networking, scalar product, unitary matrices, Hadamard transformation, 2-qubits, entanglements, manipulation of qubits, introduction to gates, example 2-qubit gates (CNOT, Pauly, swap), no-cloning theorem, Bell states and Bell measurements, quantum repeater and entanglement swapping, fundamental experiments like Mach-Zehnder interferometer.

combined (continuous and end-of-course) assessmentSeveral take-home assignments (“exercises”) will be issued and submitted by the students within 2 weeks deadlines.  Each assignment will be discussed in plenum and will be followed by small online tests which will be marked.Assessment: Final written exam (60%), online tests (20%), exercises submission and discussion (20%)

Literature and resources:Slides, comprehensive lecture notes, exercise assignments. Additional references provided during the course. All information will be made available on Moodle’s course page.

• Course title: Fundamentals of Network Theory
• Number of ECTS: 3
• Course code: MICS2-7
• Module(s): 1.3 ELECTRICAL AND COMPUTER ENGINEERING (ECE)
• Language: EN
• Mandatory: No

Split into two parts, part (A) will introduce fundamental aspects of generic mathematical networks (graphs), usually summarized as network science, and not limited to computer networking, but also applicable to networks in social media, machine learning (artificial neural networks), road traffic and vehicle communication, cryptocurrency transactions, energy transport, biology, citation networks and many more.Part (B) will explain model fundamentals of quantum networking, from basic qubit notations, Bell states, entanglements, 2-qubit gates, until quantum repeaters using entanglement swapping for multi-hop quantum communication networks.

At the end of the course, students will be able to identify and apply suitable models and graph representations with related graph metrics to analyse practical networked scenarios, and reason about most efficient application of (network centrality) metrics. Students will understand power and limits of end-to-end concepts of quantum networking applied to distributed communication scenarios including entanglement swapping.

Part (A): Eigenvectors and eigenvalues, adjacency matrix, hypergraphs and bipartite networks, trees and planar networks, degrees, paths and components, graph Laplacian.Part (B): Introduction to quantum networking, scalar product, unitary matrices, Hadamard transformation, 2-qubits, entanglements, manipulation of qubits, introduction to gates, example 2-qubit gates (CNOT, Pauly, swap), no-cloning theorem, Bell states and Bell measurements, quantum repeater and entanglement swapping, fundamental experiments like Mach-Zehnder interferometer.

Combined (continuous and end-of-course) assessmentSeveral take-home assignments (“exercises”) will be issued and submitted by the students within 2 weeks deadlines.  Each assignment will be discussed in plenum and will be followed by small online tests which will be marked.Assessment: Final written exam (60%), online tests (20%), exercises submission and discussion (20%)

Slides, comprehensive lecture notes, exercise assignments. Additional references provided during the course. All information will be made available on Moodle’s course page.

• Course title: Manufacturing systems
• Number of ECTS: 3
• Course code: MSPC-12
• Module(s): 2.1 COMMON CORE
• Language: EN
• Mandatory: Yes

• Course title: Product Planning & Marketing for Engineers
• Number of ECTS: 3
• Course code: MSPC-11
• Module(s): 2.1 COMMON CORE
• Language: EN
• Mandatory: Yes

The objective of the course is to enable engineering students to integrate a market-oriented perspective into their thinking, (product-) designing, and decision-making.

After successfully completing this module, students will be able: – to evaluate  (new) products and service offers in terms of customer value and potential market positioning; – to apply relevant marketing strategies to a given company and/or business unit; – to analyse determinants of buying behaviour and transfer this knowledge into applicable market segment strategies; – to integrate the customer perspective into the product development process;- to evaluate basic pricing and communication strategies in line with product positioning.

Basic concepts of marketingMarketing strategiesDeterminants of Buying BehaviorMarket SegmentationProduct/Brand StrategiesProduct development and positioningPricing- and Communication Strategies

Written or oral exam (70%)

Active participation (10%)

Case study solution (20%)

Literature & resources

Homburg, Ch., Küster, S., Krohmer, H. (latest edition): Marketing Management: A Contemporary Perspective, McGraw Hill

Kotler, P., Armstrong, G. (latest ed.): Principles of Marketing, Prentice Hall

• Course title: Programming for Engineers II
• Number of ECTS: 4
• Course code: MSPC-14
• Module(s): 2.1 COMMON CORE
• Language: EN
• Mandatory: Yes

The aim of the course is to teach basics of programming with modern languages (Python), software engineering fundamentals and practical applications of data analytics and visualization for engineers. The students can practically apply what they have learned in assignments and student projects along their student program or their careers.The course consists of the following learning units:·         Introduction to programming·         Concepts of programming such as object-oriented programming·         Principles of software development and UML·         Introduction to fundamental python packages used in engineering.·         Introduction to data analytics, machine learning and data visualization.

Having successfully completed the module, students will be able to demonstrate knowledge and understanding of:·         Programming algorithms for solving tasks in engineering;·         Using modern tools and methods for software development;·         Being able to process different data sets and analize them.·         Utilize visualization methods on large data records.·         Identify the required computational tools  for solving a particular engineering task.

1. Introduction to computation and programming languages (week 1)2. Elements of python programming language such as statements, operators, loops, variables, simple types, complex types (week 2 and 3) 3. Concepts of object-oriented programming, i.e. classes, objects, methods, polymorphism (week 4 and 5)4. Basics of software design (week 6 )5. Data structures and data visualization (week 7 and 8)6. Introduction to machine learning and AI tools (week 9 and 10)7. Systems design, APIs and applications (week 11)

Mid-term exam

> Written on-site examination (25%)

On-site exam that will evaluated the acquired knowledge and aptitudes of the student considering the first half of the course. It is composed by written section (e.g., multiple-choice and open question) and a coding section.

 

Final exam > Written on-site examination (40%)

On-site exam that will evaluated the acquired knowledge and aptitudes of the student considering the whole course. It is composed by written section (e.g., multiple-choice and open question) and a coding section.

 

Homework > Take-home assignments (25%)

Series of assigments that will help to reinforce the knowledge acquired during classes. They are programming tasks that evaluate the capacity of the student to extrapolate class knowledge to different situations.

Class participation (10%)

Considers that active participation of the students and their capacity of integrating themselves into class discussions. This assessment task can also consider the participation of students in forums or any platform where discussion is expected. The grade is determined by Lecturer.

• Course title: Robotics
• Number of ECTS: 4
• Course code: MSPC-13
• Module(s): 2.1 COMMON CORE
• Language: EN
• Mandatory: Yes

• Course title: Additive Manufacturing Technology
• Number of ECTS: 3
• Course code: MSPC-16
• Module(s): 2.2 MECHANICS
• Language: EN
• Mandatory: Yes

Develop a comprehensive understanding of AM technologies.

Identify and select appropriate AM technologies for particular applications as well as learn how to 3D print.

The course provides an introduction to Additive Manufacturing (AM) or 3D printing technologies, covering the background, actual methods, available printable materials and also current and future applications. AM’s global impact and the key players in AM industry will be briefly laid out. The course will consist of lectures and hands-on laboratory sessions, where students will generate 3D designs and print.

Continuous assessment•In-class Assignments (25%): Students will complete regular assignments to demonstrate their understanding of the material.•Group Projects (75%): Collaborative group tasks will be part of the course, including a presentation as a component of the assessment.

• Course title: Advanced Control
• Number of ECTS: 3
• Course code: MSPC-19
• Module(s): 2.2 MECHANICS
• Language: EN
• Mandatory: Yes

In the creation of sustainable products, control engineering plays a more and more important role. In order to realize increasingly complex functionalities and so called “smart” products, control systems with advanced information processing capabilities have to be embedded in the product. 

As an introduction, methods to model technical products and processes as sets of linear or nonlinear differential equations forming transfer function models are considered. Based on this, basic and more advanced methods to develop control systems are derived and discussed using engineering examples. Furthermore, an introduction to system-identification methods and a digital control design will be given. 

Finally, it will be discussed how basic and advanced control approaches could be simulated and tested using MATLAB software.

1. The students should have a profound knowledge how to model technical products and processes as continuous, discrete-event or hybrid dynamic systems.

2. Draws bode diagram for any system and design a compensate controllers (lead, lag) via frequency response using MATLAB.

3. The students are able to design, implement and test basic and more advanced control systems based on the derived system-identification models.

1. Introduction to dynamic system modellingbasic principlestransfer function modelstability, transient response, and steady state error. 2.  Basic linear control systemsshort repetition of basic linear SISO controlbode diagram and frequency response analysis3.  Advanced linear control systemsfeedback control system design via frequency response. digital control system design4.  Introduction to system identificationsystem identification theory data-driven control design. 5.  Simulation, Testing and Implementation of Control Systemssimulation and testing of dynamic and closed loop feedback systems using MATLAB software. 

Written examination (80%)

A written exam, including different exercises, will be done at the end of all lectures.

 

2 assignments will be given before the final exam (20%)

Take-home assignments with fixed deadlines will be given to the students to test their understanding of the lectures and to estimate their analysis capacities and how far they can look for additional information not provided in the lecture

1.Nise, Norman S. Control Systems Engineering. John Wiley & Sons, Sixth Edition,2.Richard C. Dorf, and Robert H. Bishop (2008). Modern Control Systems, Eleventh Edition, Prentice-Hall, Inc. 3.Arun K. Tangirala. Principles of System Identification Theory and Practice. (Chapter: 1-3; 12-15) 4.Lennart Ljung, System Identification: Theory for the User. Second edition.

• Course title: Advanced engineering materials
• Number of ECTS: 4
• Course code: MSPC-17
• Module(s): 2.2 MECHANICS
• Language: EN
• Mandatory: Yes

Knowledge of structural materials (metals as ferrous and nonferrous alloys; ceramics and glasses;polymers, and composites) and their use in the view of a sustainable use of resources.

The students will be capable to understand the different properties of the different key engineering materials and their use.

CrystallographyDiffusionPhase DiagramsMetals:Ferrous alloys (carbon and low-alloy steels, high-alloy steels, cast irons)Nonferrous alloys (aluminium alloys, magnesium alloys and other alloys)Processing of metals and the influence on their propertiesGlassesProcessing of ceramics and glasses and the influence on their propertiesPolymers:Thermoplastic polymersThermosetting polymersProcessing of polymersPowder Metallurgy:Cemented CarbideCermetsCompactionSinteringMechanical and Physical PropertiesMaterials and our environment:Environmental aspects of designRecycling

Written Exam 1: Intermediate exam for 25% of the total grade

Written exam 2: Final exam for 75% of the total grade

Literature & resources

Materials selection in Mechanical Design, M. F. Ashby, Butterworth-Heinemann, Burlington, 3rd ed., 2006

Engineering Materials 1 & 2, M. F. Ashby, D. R. H. Jones, 3rd ed., Butterworth-Heinemann, Burlington, 2005.

Materials Science for Engineers, J. F. Shackelford, 6th ed., Prentice Hall New Jersey, 2005.

The Science and Engineering of Materials, D. R. Askeland, 3rd ed.,Nelson Thormes Ltd., 1998.

Werkstofftechnik 1 und 2, W. Bergmann, 4. Auflage, Hanser-Verlag München, 2002.

Werkstoffe, E. Hornbogen, 7. Auflage, Springer-Verlag Berlin, 2002.

Moderne Werkstoffe, R. Gadow, A. Killinger, Expert-Verlag, 2000.

• Course title: Laser Technology for Manufacturing
• Number of ECTS: 4
• Course code: MSPC-15
• Module(s): 2.2 MECHANICS
• Language: EN
• Mandatory: Yes

You assess which laser types are suitable for which applications.You can implement concepts for new laser applications.You can list the main types of lasers.You can explain the basic terms of laser physics.You can assess the potential of laser radiation based on the process parameters.You can describe areas of industrial application of lasers

• Introduction, basics of laser, definition, laser market, laser parameters• Basic properties of laser light, light propagation, beam caustics• Laser types (gas lasers, ion lasers, solid-state lasers, fiber lasers, diodes, VCSEL)• Light and interaction with matter (absorption, impact of material, temperature)• Beam conduction in fibers and transmissive optics, remote laser delivery• Laser safety• Applications of industrial machines and prototypes of hybrid laser machines• Process technology: Laser hardening, laser welding, laser brazing• Latest research results related to polishing, and welding of dissimilar materials

Written examAdmission requirements for the exam:- Intermediate / Preliminary examinations may be determined at the beginning of the semester. In case preliminary work has been defined, it shall be provided and assessed positively before the final exam.

• Course title: Machine Design Exercise
• Number of ECTS: 3
• Course code: MSPC-18
• Module(s): 2.2 MECHANICS
• Language: EN
• Mandatory: Yes

The goal of this course is to develop the skills of students to design machine elements using gained knowledge from completed courses (Machine Design, CAD, FEA and others) and to employ this knowledge effectively in a real design project. The course focuses on engineering analyses:  analytical calculations of the strength of materials, FEA calculations, 3D design tools and finally, good project reporting.

Upon completion of the course, the student:+ is able to work in a group and knows his place and task. They can communicate with other members and a project supervisor and accept responsibility.+ they can use methods of project management.+ can carry out a technical object’s design process and meet deadlines.+ uses in practice computer-aided tools like ANSYS, Autodesk Inventor and Fusion 360.+ solves real technical problems using previously acquired knowledge of subjects: mechanics, the strength of materials, machine element design, CAD.+ is able to propose an appropriate technological process of manufacture and assembly.

The project topic is the design and analysis of a small mechanical assembly like a clutch, a torque limiter or a gear train.The steps of the project are shown below:+ Creative formulating and discovering technical problems.+ Finding solutions and analyses of issues.+ A selection of an optimum concept and its innovation.+ An accomplishment of strength calculations and technical documentation using computer-aided systems.+ Consideration of manufacturability design aspects.+ Verification of an adopted design solution to reach the prototype stage.+ Create a final report of the project ready for a public presentation.

Take-home assignment (100%)

The completed project will be evaluated by taking into account: the quality of the report, the correctness of strength calculations, self-reliance during the project, proactivity, and how to solve engineering problems in the project.

No retake evaluation can be done in winter semester / any project failed must be retaken on the following summer semester (on the following academic year).

Literature & resources

“Fundamentals of Machine Elements, Third Edition”, Steven R. Schmid, Bernard J. Hamrock, Bo. O. JacobsonCourse materials available on MoodleInventor Nastran, Autodesk manualsANSYS Workbench training materialsANSYS manualsSupplementary:”Fundamentals of Machine Components Design”, R. C. Juvinall, Kurt M. Marshek”Mark’s Calculations for Machine Design”, Thomas Brown. “Shigley’s Mechanical Engineering Design”, Richard G Budynas, Keith J Nisbett.”Engineering Drawing and Design”, 5th Edition, David A. Madsen, David P. Madsen

• Course title: Networked Feedback Systems
• Number of ECTS: 5
• Course code: MSPC-20
• Module(s): 2.3 ELECTRICAL AND COMPUTER ENGINEERING (ECE)
• Language: EN
• Mandatory: Yes

The objective of this course is to introduce students to networked feedback structures in interconnected information and communication technology in technical environments.

* Identify feedback structures, decompose them and formulate continuous and sequential dynamics

* Determine reliable discrete or continuous enclosures for structure-variations and uncertainties

* Design controls with guaranteed dynamic tolerances

* Design reliable automata in technical context.

Networked feedback and feedforward- sampling, scheduling and communication- continuous system representations- dynamics and approximations- systems over the binary field- binary transfer function and stability- combined systems and decompositions- feedback design in multiloop structures.

The grade for the course compiles 30% classwork (assignments and taking part in the lecture, attendance) and 70% exam (paper/report + presentation).

• Course title: Information Theory and Coding
• Number of ECTS: 5
• Course code: MICS2-20
• Module(s): 2.3 ELECTRICAL AND COMPUTER ENGINEERING (ECE)
• Language: EN
• Mandatory: No

The objective of this course is to provide an understanding of fundamental communication limits and means of approaching them

* Compute fundamental communication limits* Compress simple information sources* Describe the fundamental blocks of digital communication systems (physical layer)* Encode binary information with a convolutional code

The course contains: – Shannon’s concept of mathematically quantising information and uncertainty for a communication setup – Explanations that both compression and error free transmission have an extremal rate which can be computed via entropy and mutual information – Methods to compress sources – Digital transmission techniques and their complexity for inter-symbol-interference channels – Simple error correction codes, convolutional codes

Final Exam: 75% Homework: 25%

• Course title: Quality of Service in Computer Networks
• Number of ECTS: 5
• Course code: MICS2-23
• Module(s): 2.3 ELECTRICAL AND COMPUTER ENGINEERING (ECE)
• Language: EN
• Mandatory: No

The objective of this course is to introduce quantitative measures for network performance (like throughput, error correction, delays, routing) for different network topologies tobe applied to security protocols. It also sensibilises for differences between static and dynamic networks as well as centralised and de-centralised topologies concerning reliability and trust issues

* Describe performance metrics and list parameters of dedicated networks and protocols.* Name and reproduce definitions of relevant parameters that theoretically characterise the communication traffic incl. queues, routing and error probabilities* Analyze existing solutions according to their capabilities for throughput, error rate and security* Construct and adapt real world communication architectures and protocols with given Quality of Service requirements on the basis of the theoretical concepts

1. Intro2. Recap: Random Processes3. Recap: Homogeneous Markov Chains4. Commutation Systems: Components and modules5. Communication Traffic as Random Process6. Routing and Flow Control7. Introduction to Queueing Theory8. QoS in TCP/IP

70% Final Exam30% Successful preparation, submission and active participation in exercise sessions

• Course title: Advanced Project / Case Study
• Number of ECTS: 12
• Course code: MSP-30
• Module(s): 3.1 COMMON CORE
• Language: EN
• Mandatory: Yes

Purpose of the case study in the third master semester is to apply your engineering learnings but even more relevant to learn scientific work, and thus to prepare your Master project.

To ensure the desired broad learning, we require that the case study and the Master thesis are distinct, thus you shall work on two different projects with two different supervisors.

Written report + 15 mins. presentation.+ 5 mins Q&A

BE CAREFUL: In order to ensure broad education, we require Advanced Project / Case Study & Master Thesis being supervised by different Professors.

• Course title: Design for Circularity
• Number of ECTS: 2
• Course code: MSP-66
• Module(s): 3.1 COMMON CORE
• Language: EN
• Mandatory: Yes

Comply with circular economy obligations & understand End-of-Life needs.

Data collection of end-of-life process & circular product designs adapted to it.

 

 

Theoretical background & practical group application.Visit:The aim is to give the students a first impression of possible design shortcomings that make it more difficult to repair or that are responsible for a faster need for repair. The visit is also part of the data collection for their group work, which they have to present at the end of the semester. Lectures:In the 4 lectures, students are taught the necessary theoretical background to the circular economy. The aim is to give them a clear understanding of the future requirements of engineering tasks (opportunities for future jobs) and the requirements from a regulatory perspective. The first lecture will give an introduction to the circular economy. The second lecture will focus on the end-of-life processes to give students an insight into how different products are sorted, recycled or dismantled. The third lecture deals with the beginning of life, i.e. the design and the newly required product passports. The fourth and final session will focus on best practices currently used in the industry. Workshops:In the two workshops, the theoretical knowledge gained from the lectures will be applied in practice. During the two-day block course, Lecture 2 and Lecture 3 are always followed by a practical workshop. The first workshop focuses on the data collection of end-of-life processes, where the students have to analyze an industrial recycling process. The second block course focuses on the design part, where students fill out a Product Circularity Data Sheet (PCDS) and rethink the product designs that go through the previously analyzed recycling process. Feedback & Discussion sessions:During the three feedback sessions, the groups need to present the advancement of their works to all the others. The first session will before the discussion with experts from CELL and RepairCafé, in order to prepare for the expert session. The second feedback session is together with experts from CELL as well as RepairCafé. The final feedback session is before the pitching sessions, where the students will get support from an expert of the incubator about how to prepare their final pitch. Group Works:In the first lecture, the students are divided into different groups of 3 to 4 people and have to decide on a specific product category (e.g. electronic devices, clothing, …). During the semester, the different groups have to collect information about the whole life cycle of the product group and develop new design features that increase the circularity of the product. There are several tasks to fulfill: First, they have to collect data on the end-of-life processes of the product group, which includes the RepairCafé in Walferdange. Then they have to analyze different life cycle possibilities of the product group before developing design features to improve circularity. Finally, they have to find a way to share their knowledge with potential customers. The results of the group work are presented in the final pitching session as well as in a small report of maximum 10 pages.

Assessment tasks

Type of assessment

Grading scheme

Weight for final grade

Task 1

Attendance

 

Pass/Fail

20%

Objectives

Interactive course, so attendance is mandatory & important

 

Assessment rules

At least 80% presence need to be reached

 

Assessment criteria

If not at least 80% is reached, no points will be given

 

Task 2

Presentation

 

20 points (0-20)

50%

Objectives

Final group presentation in the form of a pitch

 

Assessment rules

Presentation should be maximum of 20 minutes

 

Assessment criteria

Based on presentation clarity & results

 

Task 3

Take-home assignment

20 points (0-20)

30%

Objectives

Next to presentation, final report will be graded as well

 

Assessment rules

Clearly structured report & maximum of 10 pages

 

Assessment criteria

Quality of the report

 

Literature:

 

1)Ellen MacArthur Foundation:-https://www.ellenmacarthurfoundation.org

 

2)RepairCafe:-https://www.repaircafe.lu/  -https://www.repairmonitor.org/

 

3)PCDS:-https://terramatters.net/fr/a-propos

 

4)Scientific Papers:-https://www.mdpi.com/2313-4321/8/6/89  -https://www.sciencedirect.com/science/article/pii/S0959652622048612

 

5)Legislation:- https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/european-green-deal_en – https://commission.europa.eu/energy-climate-change-environment/standards-tools-and-labels/products-labelling-rules-and-requirements/sustainable-products/ecodesign-sustainable-products-regulation_en – https://environment.ec.europa.eu/topics/circular-economy/green-claims_en – https://environment.ec.europa.eu/topics/waste-and-recycling/packaging-waste_en – https://www.iso.org/committee/7203984.html

• Course title: Integrated management systems
• Number of ECTS: 3
• Course code: MSP-57
• Module(s): 3.1 COMMON CORE
• Language: EN
• Mandatory: Yes

Learn contents of management systems and to act as an auditee in (certif.) audits.

Can act as an auditee in (certif.) audits; knows, what it is all about IMS in meetings.

Chapters of management system standards, processes and procedures, background.

Written exam – 100%

Literature & resources

-Quality Management e-learning Deutsch/English, WEKA-Verlag-Tilo Pfeifer / Robert Schmitt (Hrsg): Masing Handbuch Qualitätsmanagement, Hanser-Verlag, 16. Ausgabe 2021-Tilo Pfeifer: Production Metrology, De Gruyter 2002-The EFQM Model 2020, www.EFQM.org , free Download

• Course title: Scientific writing and presentation skills
• Number of ECTS: 3
• Course code: MSP-61
• Module(s): 3.1 COMMON CORE
• Language: EN
• Mandatory: Yes

This course aims to give students the background and confidence to write effective scientific reports.

The students will learn the fundamentals of effective scientific and professional writing. Presentation skills, verbal and non-verbal communication as well as specific documents such as the executive summaries will be covered.  

At the end of the course the student should be able to express ideas in a clear, coherent and concise written form, gain control over nonverbal language during an oral presentation and interact with the audience.  

Presentation skills. Scientific writing skills. 

Task 1: take-home assignement (33.3%)

Objective: Deliver all assignments or homeworks

Criteria: Apply the rules for concise, coherent and positive writing

 

 

Task 2: take-home assignement (33.3%)

Objective: Perform a 7 minute oral presentation

Criteria: Apply the rules for eye contact, intonation, presentation layout and body language

Assessment rules: The text will be graded according to the criteria defined in the sessions

 

 

Task 3: take-home assignement (33.3%)

Objective: Write a report

Criteria: Apply the rules for coherence, clarity and technical content

Assessment rules: The text will be graded according to the criteria defined in the sessions

 

Mandatory attendance

Literature and resources

All literature and resources are given during the sessions in form of a script.

• Course title: Mandatory 10 weeks industrial internship
• Number of ECTS: 0
• Course code: MSP-64
• Module(s): 3.1 COMMON CORE
• Language:
• Mandatory: Yes

• Course title: Sensors & signal processing [old]
• Number of ECTS: 3
• Course code: MSP-46
• Module(s): 3.2 MECHANICS
• Language: EN
• Mandatory: No

Understand the basic metrology, sensor concepts and signal processing methods in the context of engineering.

Gain a basic understanding of sensor and signal processing concepts.

Basic SI units, electrical circuits, linear time-invariant systems, Fourier analysis, analog and digital sensor data acquisition, introduction to fundamental sensor types.

 Task 1 – Active participation 10%Objectives: Encouraging students in class activities to improve their thinking skills and introduce new concepts to their peers.Assessment criteria: Student performance and participation in the classroom Task 2 – Take-home assignment 40%Objectives: Examination of students’ skills and understanding of the material taught in class.Assessment rules: The assignments must be completed and submitted by the deadline. Assessment criteria: The evaluation depends on understanding of the subject and the concept, the final results, mathematical approach, etc. Task 3 – Written exam 50%Objectives: Test the knowledge, skills, and aptitude acquired by the students.Assessment rules: In-class test of various types of questions, 1.5-hour duration. Assessment criteria: The mathematical approach, presentation and the final answer.

Literature listOptional:Jacob Fraden, Handbook of Modern Sensors: Physics, Designs, andApplications, Springer 2010, ISBN: 978-1441964656 

• Course title: Electrical Energy Production Transportation and Distribution
• Number of ECTS: 3
• Course code: MSP-44
• Module(s): 3.2 MECHANICS
• Language: EN
• Mandatory: No

1. Knowing the different sources of energy contributing to the production of the electrical energy2. Knowing the different solutions to network the power units together and with the consumer3. Knowing the electrical and the mechanical conversion possibilities for the distribution of the electrical energy4. Understanding the electrical power flow management between the power units together as well as with the consumer5. Knowing the electrical power quality norms6. Knowing the power losses generation and its relative cost in the energy systems for a sustainableand rational use of the electrical energy

The student will acquire a global knowledge about the production, transportation, distribution and conversion of the electrical energy, as well as its transformation into/from the mechanical energy.The sustainable rational use of the electrical energy as well as the electrical energy management, are also covered.

1. Production of the electrical energy• The energy sources (fossil fuels, nuclear, renewable)• The generation of the electrical energy2. Transportation of the electrical energy• Electrical power transmission• Power quality norms• Low to high DC and AC voltage grids• Coupling of voltage supplies3. Conversion and distribution of the electrical energy• Modern distribution systems• Transformation of the electrical energy4. Sustainable and rational use of the electrical energy• Power losses• Costs of the energy systems

Written exam (100%)Objectives:1.To be able to define the fundamental electrical devices used for the production, transformation, transport and distribution of electrical energy2.To be able to explain the operation of the electrical devices3.To be able to calculate relevant values of electrical circuits made of passive components Assessment rules:Based on application exercises (theory, formal calculation & simulation) and their correction during the lecture, the student must be able to answer questions and solve similar problems on his/her own. Assessment criteria:Quality of the answers consisting in a correct communication language (English), the detailed methodology and the calculation results.

Available at the University library or on internet:[1] http://en.wikipedia.org/wiki/Electric_power[2] James Northcote-Green, Robert Wilson, “Control and Automation of Electrical Power DistributionSystems”, Taylor & Francis 2007, ISBN 0-8247-2631-6[3] Peter Zacharias, “Use of Electronic-Based Power Conversion for Distributed and RenewableEnergy Sources”, ISET 2008[4] Adolf J. Schwab, “Elektroenergiesysteme – Erzeugung, Transport, Übertragung und Verteilungelektrischer Energie“, Springer 2008, ISBN 3-540-29664-6[5] H.J. Haubrich, G. Henneberger, H.C. Skudelny, Müller-Hellmann, “Elektrische Energie ausregenerativen Quellen“, Vorlesung der RWTH Aachen 1994[6] Andreas Wagner, “Photovoltaik Engineering – Handbuch für Planung, Entwicklung undAnwendung“, Springer 2006, ISBN 3-540-30732-X[7] Mark Hankins, “Stand-alone Solar Electric Systems“, Earthscan 2010, ISBN 978-1-84407-713-7[8] Michael Fette, Rolf Schwarze, Jürgen Voß, “Energieversorgung der Zukunft“, VDE Verlag 1996,ISBN 3-8007-2174-0

• Course title: Energetics of the blast furnace/Paul Wurth
• Number of ECTS: 3
• Course code: MEEE-5
• Module(s): 3.2 MECHANICS
• Language: EN
• Mandatory: No

Introduction of industrial processes to the students in order to bridge the theory of the study and the industrial application. Technical, environmental and economical aspects are discussed and the interrelationship shall become obvious.

Understanding of overall iron making procedure and how to reduce its CO2 emissions

 The Blast Furnace Process:·         History and description of the Blast Furnace·         The Blast Furnace Process:-       Reduction Equations-       Thermal and mass balance·         Auxiliary plants (Hot Stoves, Sinter Plant, Pulverized Coal Injection Plant, Slag treatment, etc.) Technical Improvements to the Blast Furnace Process with economical and environmental impacts:·         Top Gas Recovery Turbine·         Heat recovery system at the Hot Stoves.         Digitalization.         Technologies for CO2 emissions reduction-          Reducing gas generation-          CO2 capture 

TThe students need to accomplish a project. The project presentation and final report willbe considered for the assessment. There are also assignment, which should be submitted in written form.Take-home assignment -5% of Final Grade  Written exam – 75 % of Final GradeGroup work – 20% of Final Grade   

The students need to accomplish a project. The project presentation and final report would be considered for the assessment. There are also assignment, which should be submitted in written form.The presentations are shared with the students

• Course title: Sensors & signal processing
• Number of ECTS: 3
• Course code: MSPC-9
• Module(s): 3.2 MECHANICS
• Language: EN
• Mandatory: Yes

Understand the basic metrology, sensor concepts and signal processing methods in the context of engineering.

Gain a basic understanding of sensor and signal processing concepts.

Basic SI units, electrical circuits, linear time-invariant systems, Fourier analysis, analog and digital sensor data acquisition, introduction to fundamental sensor types.

Active participation (10%)

Objectives: Encouraging students in class activities to improve their thinking skills and introduce new concepts to their peers.

Assessment criteria: Student performance and participation in the classroom

Take-home assignment (40%)

Objectives: Examination of students’ skills and understanding of the material taught in class.

Assessment rules: The assignments must be completed and submitted by the deadline. 

Assessment criteria: The evaluation depends on understanding of the subject and the concept, the final results, mathematical approach, etc.

Written exam (50%)

Objectives: Test the knowledge, skills, and aptitude acquired by the students.

Assessment rules: In-class test of various types of questions, 2-hour duration. To pass the course the student needs to score at least 40% in the final exam and have gain at least 10 points out of 20 in total.

Assessment criteria: The mathematical approach, presentation, and the final answer.

 

Mandatory attendance.

Literature & resources

Lecture slidesOwn written notes from lecturesOptional:Jacob Fraden, Handbook of Modern Sensors: Physics, Designs, and Applications, Springer 2010, ISBN: 978-1441964656 

• Course title: Artificial Intelligence
• Number of ECTS: 5
• Course code: MSP-54
• Module(s): 3.3 ELECTRICAL AND COMPUTER ENGINEERING (ECE)
• Language: EN
• Mandatory: No

Acquire general knowledge on the objectives and application domains of Artificial Intelligence, the underlying principles behind learning models, decision systems, and problem solving tools.

Understand the purpose and role of Artificial Intelligence in real life today.

Compare and contrast various Artificial Intelligence tools and techniques, ranging from search algorithms to deep learning.

Choose the right tool to solve a given task.

Evaluate the performance of the applied algorithms and the constructed models based on reliable measures and metrics.

After attending this class, the students can describe and explain the principles behind the main Artificial Intelligence techniques, tools, models and algorithms. 

The students understand the hypotheses and assumptions behind each technique and can reasonably predict the consequences of these assumptions. 

The students are capable of choosing the right tool for the job to solve a given problem.

Having chosen the optimal Artificial Intelligence technique, the students can use it to the model the problem efficiently. The students can then implement the model using their preferred programming language, tool, or software.

The students can prepare and pre-process the data related to the problem. The students can identify existing biases and know how to avoid and/or remove them. The students can act correctly to handle missing and/or corrupted data. The students understand the importance of data, and of correct and efficient data gathering techniques.

The students can correctly evaluate the performance of the model using several metrics depending on the task and problem. The students can compare the performance to that of other models. The students can verify if their underlying assumptions are correct. The students are capable of reviewing the effectiveness of the chosen technique and identifying potential improvements.

The students can present the chosen solution, the obtained model, the performance evaluation, and the identified improvements in a precise and concise fashion.

The course includes the following topics:1. General introduction to Artificial Intelligence2. Problem resolution, search algorithms3. Games, alpha-beta pruning4. Meta-heuristics, genetic algorithms, swarm algorithms5. Constraint programming6. Markov Decision Processes, reinforcement learning7. Learning models for regression, classification, clustering8. Evaluating the performance of a learning model9. Decision trees, forests10. Artificial neural networks11. Unsupervised learning, k-Nearest neighbors, self-organizing maps, growing neural gas

Pass Criteria:

Student exceeds expectations (excellent) = 20 – 18 points

Student accomplished task fully as expected = 17.9 – 15 points 

Student shows basic understanding and accomplished task as expected = 14.9 – 12 points

Students shows basic understanding and can execute the task sufficiently with some flaws = 11.9 – 10 points

 

Fail Criteria:

Performance is below standard/expectations but student shows some basic knowledge = 9.9 – 8 points

Performance is below standard/expectations, no basic knowledge of topic/procedure = 7.9 – 0 points

 

 

Task 1: 

Oral Presentation Resulting from a Group Project (70%)

The students will be divided into groups and will choose one project from a selection. Each project will involve studying a real-world problem and applying the Artificial Intelligence techniques discussed in class in order to solve it. The objective is to gain hands-on experience facing these problems and using these techniques, and to simulate real-world usage of Artificial Intelligence.

Each group will present the results of the project in a short 5/10-minute start-up pitch-like presentation. The focus of the presentation should be on the design choices made, the assumptions and hypotheses of the solution, the parameter choice and value optimisation, the analysis of the results, the evaluation of solution performance, and the limitations and future improvements envisaged.

The projects will be assessed using peer-review: each group will evaluate two other projects, different than theirs. This is to get exposures on at least three different problems and solutions. The course coordinator will also participate in the evaluation, and the mark will be the weighted average between the groups and the coordinator. To aid with the peer-evaluation process, the students will receive an evaluation grid along with the project descriptions.

 

Task 2: Take-home assignments (30%)

The course includes continuous assessment in the form of graded Tutorial exercises.

 

 

 

 

 

Literature:

Script, recommended literature in library of UL, exercises, TD, lab sessions

Bishop, C. M. (2006). Pattern recognition and machine learning. springer.

Mitchell, T. M. (1997). Machine learning. 1997. McGraw Hill

Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. Pearson Education Limited,.

Sutton, R.S., & Barto, A.G. (1998). Reinforcement Learning: An Introduction. Cambridge: The MIT Press. (https://muse.jhu.edu/book/60836)

• Course title: Estimation approaches in advanced engineering systems
• Number of ECTS: 4
• Course code: MSP-60
• Module(s): 3.3 ELECTRICAL AND COMPUTER ENGINEERING (ECE)
• Language: EN
• Mandatory: No

Conventional and unconventional approaches for the control and observation of engineering systems with different applications in energy, medicine, industry, robotics, etc.

At the end of this course the student is able to:• Analyze and develop different types of models: white box, black box and hybrid models• Develop internal representation and knowing its advantage compared to the external representation• Analyze the controllability and observability of systems.• Develop an estimator based on physical equations describing the studied system.• Develop an estimator by signal approach• Process and analyze the data to develop an intelligent, linear and non-linear estimators and classifiers.

1. Introduction1.1. System concept1.2. Modelling concepts (black box, white box and gray box  models, usefulness of the model for controlling and for observation)1.3. Measurement and observation concepts 2. Deterministic Parameter Estimation2.1    Recursive Least Squares2.2    Properties of RLS 3. State Space representation3.1. Open loop and closed loop concepts3.2    Reminder on the Transfer Function3.3. General principle of State Space representation3.4. From Transfer Function to State Space representation3.5. From State Space representation to transfer function 4. Observability and Controllability of a system4.1. Definition4.2. Kalman criteria 4.3. System controllability and observability studies 5. Conventional estimation approach5.1. Principle of observation5.2. Condition of existence of an observer5.3. Construction of a state observer: Luenberger observer 6. Bayesian Approach6.1. Primer on Multivariate Random Processes6.2. State space modelling of random processes6.3.   The Kalman filter 7.     Applications of AI7.1. Primer on Learning methods7.2. Linear and Non-linear Regression7.3.   Clustering and Classification 7.4.   Design Methodologies7.5    Examples

Active participation (10%)

Objectives: Encouraging students in class activities to improve their thinking skills and introduce new concepts to their peers.

Assessment criteria: Student performance and participation in the classroom

Take-home assignment (40%)

Objectives: Examination of students’ skills and understanding of the material taught in class.

Assessment rules: The assignments must be completed and submitted by the deadline.

Assessment criteria: The evaluation depends on understanding of the subject and the concept, the final results, mathematical approach, etc.

Written exam ( 50%)

Objectives: Test the knowledge, skills, and aptitude acquired by the students.

Assessment rules: In-class test of various types of questions, 2-hour duration. To pass the course the student needs to score at least 50% in the final exam.

Assessment criteria: You must have a 10/20 to validate the course.

Literature & resources

Lecture slidesOwn written notes from lecturesOptional1.Modern control Engineering Ogata, Prentice Hall 2.Automatic Control Systems B.C Kuo , John Wiley and Sons3.Adaptive Filter Theory, Simon Haykin, Prentice Hall

• Course title: Master thesis
• Number of ECTS: 30
• Course code: MSP-31
• Module(s): Master thesis
• Language: FR
• Mandatory: Yes